Inferring intent from pose and speech input

ABSTRACT

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program, and a method for performing operations comprising receiving an image that depicts a person, identifying a set of skeletal joints of the person and identifying a pose of the person depicted in the image based on positioning of the set of skeletal joints. The operations also include receiving speech input comprising a request to perform an AR operation and an ambiguous intent, discerning the ambiguous intent of the speech input based on the pose of the person depicted in the image and in response to receiving the speech input, performing the AR operation based on discerning the ambiguous intent of the speech input based on the pose of the person depicted in the image.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example intent determinationsystem, according to example examples.

FIGS. 6, 7, and 8 are diagrammatic representations of outputs of theintent determination system, in accordance with some examples.

FIG. 9 is a flowchart illustrating example operations of the intentdetermination system, according to some examples.

FIG. 10 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 11 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, VR and AR systems display images representing a given user bycapturing an image of the user and, in addition, obtaining a depth mapusing a depth sensor of the real-world human body depicted in the image.By processing the depth map and the image together, the VR and ARsystems can detect positioning of a user in the image and canappropriately modify the user or background in the images. While suchsystems work well, the need for a depth sensor limits the scope of theirapplications. This is because adding depth sensors to user devices forthe purpose of modifying images increases the overall cost andcomplexity of the devices, making them less attractive.

Certain systems do away with the need to use depth sensors to modifyimages. For example, certain systems allow users to replace a backgroundin a videoconference in which a face of the user is detected.Specifically, such systems can use specialized techniques that areoptimized for recognizing a face of a user to identify the background inthe images that depict the user's face. These systems can then replaceonly those pixels that depict the background so that the real-worldbackground is replaced with an alternate background in the images.However, such systems are generally incapable of recognizing a wholebody of a user. As such, if the user is more than a threshold distancefrom the camera such that more than just the face of the user iscaptured by the camera, the replacement of the background with analternate background begins to fail. In such cases, the image quality isseverely impacted, and portions of the face and body of the user can beinadvertently removed by the system as the system falsely identifiessuch portions as belonging to the background rather than the foregroundof the images. Also, such systems fail to properly replace thebackground when more than one user is depicted in the image or videofeed. Because such systems are generally incapable of distinguishing awhole body of a user in an image from a background, these systems arealso unable to apply visual effects to certain portions of a user'sbody, such as converting, blending, transforming, changing or morphing abody part (e.g., a face) into an AR graphic.

Some AR systems allow users to perform various AR operations using theirvoice. Namely, the AR systems employ speech recognition engines toconvert voice or speech input into suitable commands. While such systemsgenerally work well, they require users to follow strict formattingguidelines when providing the speech input. If the speech input that isreceived fails to comply with the guidelines, the AR systems fail toperform the correct AR operations or fail to recognize the appropriatecommands. For example, if the speech that is received includes one ormore pronouns or adverbs, the AR system is unable to discern the correctoperation to perform as the AR system is incapable of disambiguating theintent of the pronouns or adverbs. This prevents users from freelyspeaking their interests and takes away from the enjoyment ofcontrolling AR systems using speech input. As a result, resourcesdedicated to performing speech recognition are wasted.

The disclosed techniques improve the efficiency of using the electronicdevice by performing skeletal tracking for a person depicted in an imageand determining a pose associated with the skeletal tracking. Thedisclosed techniques can receive speech input that includes an ambiguousintent, such as one or more pronouns or adverbs, and that requests an ARoperation to be performed. The disclosed techniques can leverage thepose associated with the person depicted in the image to discern anddisambiguate the intent of the speech input e.g., to identify an object,item, subject, direction or quantity represented by the pronouns oradverbs). After disambiguating the intent of the speech input, thedisclosed techniques can perform a requested AR operation. Thissignificantly improves the operation of the AR systems by allowing auser to control the AR systems using their voice. Also, because theusers are not required to learn and follow strict voice controlguidelines, the learning curve of using the disclosed AR system issignificantly reduced, which enables more users to operate the disclosedAR system using voice control This avoids wasted resources and improvesthe overall functioning of the device.

Namely, the disclosed techniques simplify the process of adding ARgraphics to an image or video based on speech, which significantlyreduces design constraints and costs in generating such AR graphics anddecreases the amount of processing complexities and power and memoryrequirements. This also improves the illusion of the AR graphics beingpart of a real-world environment depicted in an image or video thatdepicts the object. This enables seamless and efficient addition of ARgraphics to an underlying image or video in real time on small scalemobile devices. The disclosed techniques can be applied exclusively ormostly on a mobile device without the need for the mobile device to sendimages/videos to a server. In other examples, the disclosed techniquesare applied exclusively or mostly on a remote server or can be dividedbetween a mobile device and a server.

As a result, a realistic display can be provided that adds an AR graphicto an image or video while a person moves around a video in threedimensions (3D), including changes to the body shape, body state, bodyproperties, position, and rotation, in a way that is intuitive for theuser to interact with and select using their voice. As used herein,“article of clothing,” “fashion item,” and “garment” are usedinterchangeably and should be understood to have the same meaning. Thisimproves the overall experience of the user in using the electronicdevice. Also, by providing such AR experiences without using a depthsensor, the overall amount of system resources needed to accomplish atask is reduced.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108, and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications, such as external apps 109 using Application ProgrammingInterfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces of the messaging client 104.

Turning now specifically to the messaging server system 108, an APIserver 116 is coupled to, and provides a programmatic interface to,application servers 114. The application servers 114 are communicativelycoupled to a database server 120, which facilitates access to a database126 that stores data associated with messages processed by theapplication servers 114, Similarly, a web server 128 is coupled to theapplication servers 114 and provides web-based interfaces to theapplication servers 114. To this end, the web server 128 processesincoming network requests over the Hypertext Transfer Protocol (HTTP)and several other related protocols.

The API server 116 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationservers 114. Specifically, the API server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The API server 116 exposes various functionssupported by the application servers 114, including accountregistration; login functionality; the sending of messages, via theapplication servers 114, from a particular messaging client 104 toanother messaging client 104; the sending of media files (e.g.; imagesor video) from a messaging client 104 to a messaging server 118, and forpossible access by another messaging client 104; the settings of acollection of media data (e.g., story); the retrieval of a list offriends of a user of a client device 102; the retrieval of suchcollections; the retrieval of messages and content; the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph); the location of friends within a social graph; and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including, for example, a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2 ). Scan functionalityincludes activating and providing one or more AR experiences on a clientdevice 102 when an image is captured by the client device 102.Specifically, the messaging client 104 on the client device 102 can beused to activate a camera. The camera displays one or more real-timeimages or a video to a user along with one or more icons or identifiersof one or more AR experiences. The user can select a given one of theidentifiers to launch the corresponding AR experience or perform adesired image modification (e.g., replace or change a garment being wornby a user in a video or morph, change, blend or transform a portion of abody of a person or user into an AR graphic, such as an AR werewolf orAR bat).

In some cases, the user can speak a command to activate a given ARexperience by providing speech input. The client device 102 includes amicrophone that can receive the speech input. The client device 102 canprocess the speech input and detect an ambiguity in an intent of thespeech input, such as by identifying one or more pronouns or adverbs.The client device 102 can receive body pose information for a userdepicted in the image or video and can discern or disambiguate theintent using the body pose. Namely, the client device 102 can perform arequested AR operation in the speech input by identifying a correctobject, item, place, or direction referenced by the pronoun or adverb inthe speech input.

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. within thedatabase 126. Examples of functions and services supported by the socialnetwork server 124 include the identification of other users of themessaging system 100 with which a particular user has relationships oris “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102. (e.g., a “native app”) or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from an external app(s) server 110, a markup-languagedocument associated with the small-scale external application andprocessing such a document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client 104, with the ability to share an item, status, state,or location in an external resource with one or more members of a groupof users into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

The messaging client 104 can present to a user one or more ARexperiences based on speech or voice input received from the user. As anexample, the messaging client 104 can detect a person or user in animage or video captured by the client device 102. The messaging client104 can generate a skeleton for the person that identifies skeletal keypoints of the body. Based on the skeletal key points, the messagingclient 104 can determine a pose of the person depicted in the image orvideo. Based on the determined pose, the messaging client 104 candiscern one or more pronouns or adverbs to disambiguate an intent ofreceived speech input that includes a command to perform an ARoperation. This allows a user to input simple and ambiguous voicecommands and the messaging client 104 can correctly execute therequested commands based on additional information that includes a bodypose of the user. As an example, the messaging client 104 can receive avoice command that specifies “change this to blue”. In this case, themessaging client 104 can determine that the speech input is ambiguous asto the object being referenced by the pronoun “this”. The messagingclient 104 can access pose information about the speaker of the speechinput and determine that the pose of the speaker includes a reference toa hat worn by the person. As an example, the pose includes a handpointing in a particular direction. The messaging client 104 candetermine that the particular direction intersects a hat or otherfashion item worn by the person. In response, the messaging client 104resolves the ambiguous intent, namely the pronoun “this”, with theobject being pointed at by the speaker, in this case the hat or otherfashion item. The messaging client 104 then executes or performs therequested AR operation to change a color of the hat or other fashionitem to the color blue. In particular, the messaging client 104 segmentsthe article of clothing using a suitable article or clothingsegmentation process and recolors the pixels of the article of clothingthat are inside of the segmentation with a blue color. Further detailsof the AR control process using speech input are provided below inconnection with FIG. 5 .

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata), A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 further includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain augmented reality experiencesand presents identifiers of such experiences in one or more userinterfaces (e.g., as icons over a real-time image or video or asthumbnails or icons in interfaces dedicated for presented identifiers ofaugmented reality experiences). Once an AR experience is selected, oneor more images, videos, or AR graphical elements are retrieved andpresented as an overlay on top of the images or video captured by theclient device 102. In some cases, the camera is switched to afront-facing view (e.g., the front-facing camera of the client device102 is activated in response to activation of a particular ARexperience) and the images from the front-facing camera of the clientdevice 102 start being displayed on the client device 102 instead of therear-facing camera of the client device 102. The one or more images,videos, or AR graphical elements are retrieved and presented as anoverlay on top of the images that are captured and displayed by thefront-facing camera of the client device 102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 including the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, and functions (e.g.,commands to invoke functions) as well as other payload data (e.g., text,audio, video, or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104 and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game by issuing invitations to such other users from themessaging client 104, The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes APIs withfunctions that can be called or invoked by the web-based application. Incertain examples, the messaging server 118 includes a. JavaScriptlibrary that provides a given third-party resource access to certainuser data of the messaging client 104. HTML5 is used as an exampletechnology for programming games, but applications and resourcesprogrammed based on other technologies can be used.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., only 2Davatars of users with or without different avatar characteristics). Asanother example, external resources that include small-scale versions ofexternal applications (e.g., web-based versions of third-partyapplications) are provided with access to a second type of user data(e.g., payment information, 2D avatars of users, 3D avatars of users,and avatars with various avatar characteristics). Avatar characteristicsinclude different ways to customize a look and feel of an avatar, suchas different poses, facial features, clothing, and so forth.

An intent determination system 224 receives an image depicting one ormore users and speech input. The intent determination system 224determines that the speech input includes an ambiguous intent (e.g.,includes a pronoun or adverb). The intent determination system 224determines a pose of the one or more users depicted in the image toresolve or discern the ambiguous intent. Once the ambiguous intent isresolved or discerned, the intent determination system 224 performs oneor more AR operations requested in the speech input based on the object,subject, item, or direction referenced by the ambiguous intent of thespeech input. In one example, the intent determination system 224changes, blends, deforms, transforms, or morphs at least one real-worldobject using one or more AR elements.

Specifically, the intent determination system 224 is a component thatcan be accessed by an AR/VR application implemented on the client device102 in response to a spoken command to perform an AR/VR operation. TheAR/VR application uses an RGB camera to capture a monocular image of auser. The AR/VR application applies various trained machine learningtechniques on the captured image of the user to determine a pose of theuser depicted in the image to disambiguate one or more spoken commandsand to apply one or more AR visual effects to the captured image basedon the spoken commands.

In training, the intent determination system 224 obtains a firstplurality of input training images that include depictions of one ormore users having different poses and speech input with an ambiguousintent (e.g., a pronoun or an adverb). These training images alsoprovide the ground truth information including the object, direction,item, or subject corresponding to the ambiguous intent of the speechinput. A machine learning technique (e.g., a deep neural network) istrained based on features of the plurality of training images.Specifically, the machine learning technique extracts one or morefeatures from a given training image and estimates an object, direction,item, or subject corresponding to the ambiguous intent of the speechinput for the pose depicted in the given training image. The machinelearning technique obtains the ground truth information including theobject, direction, item, or subject corresponding to the training imageand adjusts or updates one or more coefficients or parameters to improvesubsequent estimations of the intent (e.g., the object, direction, item,or subject corresponding to an ambiguous intent of speech input). Themachine learning technique continues processing additional training dataand updating parameters until one or more stopping criteria is met.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload, further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316, Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based, or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, and settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100 and onmap interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208, Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 107.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes AR content items (e.g., corresponding to applying augmentedreality experiences). An AR content item or AR item may be a real-timespecial effect and sound that may be added to an image or a video.

As described above, augmentation data includes AR content items,overlays, image transformations, AR images, AR logos or emblems, andsimilar terms that refer to modifications that may be applied to imagedata (e.g., videos or images). This includes real-time modifications,which modify an image as it is captured using device sensors (e.g., oneor multiple cameras) of a client device 102 and then displayed on ascreen of the client device 102 with the modifications. This alsoincludes modifications to stored content, such as video clips in agallery that may be modified. For example, in a client device 102 withaccess to multiple AR content items, a user can use a single video clipwith multiple AR content items to see how the different AR content itemswill modify the stored clip. For example, multiple AR content items thatapply different pseudorandom movement models can be applied to the samecontent by selecting different AR content items for the content.Similarly, real-time video capture may be used with an illustratedmodification to show how video images currently being captured bysensors of a client device 102 would modify the captured data. Such datamay simply be displayed on the screen and not stored in memory, or thecontent captured by the device sensors may be recorded and stored inmemory with or without the modifications (or both). In some systems, apreview feature can show how different AR content items will look withindifferent windows in a display at the same time. This can, for example,enable multiple windows with different pseudorandom animations to beviewed on a display at the same time.

Data and various systems using AR content items or other such transformsystems to modify content using this data can thus involve detection ofreal-world objects (e.g., faces, hands, bodies, cats, dogs, surfaces,objects, etc.), tracking of such objects as they leave, enter, and movearound the field of view in video frames, and the modification ortransformation of such objects as they are tracked. In various examples,different methods for achieving such transformations may be used. Someexamples may involve generating a 3D mesh model of the object or objectsand using transformations and animated textures of the model within thevideo to achieve the transformation. In other examples, tracking ofpoints on an object may be used to place an image or texture (which maybe 2D or 3D) at the tracked position. In still further examples, neuralnetwork analysis of video frames may be used to place images, models, ortextures in content (e.g., images or frames of video). AR content itemsor elements thus refer both to the images, models, and textures used tocreate transformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object's elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh and then variousareas based on the points are generated. The elements of the object arethen tracked by aligning the area for each element with a position foreach of the at least one element, and properties of the areas can bemodified based on the request for modification, thus transforming theframes of the video stream. Depending on the specific request formodification, properties of the mentioned areas can be transformed indifferent ways. Such modifications may involve changing color of areas;removing at least some part of areas from the frames of the videostream; including one or more new objects into areas which are based ona request for modification; and modifying or distorting the elements ofan area or object. In various examples, any combination of suchmodifications or other similar modifications may be used. For certainmodels to be animated, some characteristic points can be selected ascontrol points to be used in determining the entire state-space ofoptions for the model animation.

In some examples of a computer animation model to transform image datausing body/person detection, the body/person is detected on an imagewith use of a specific body/person detection algorithm (e.g., 3D humanpose estimation and mesh reconstruction processes). Then, an ASMalgorithm is applied to the body/person region of an image to detectbody/person feature reference points.

Other methods and algorithms suitable for body/person detection can beused. For example, in some examples, features are located using alandmark, which represents a distinguishable point present in most ofthe images under consideration. For body/person landmarks, for example,the location of the left arm may be used. If an initial landmark is notidentifiable, secondary landmarks may be used. Such landmarkidentification procedures may be used for any such objects. In someexamples, a set of landmarks forms a shape. Shapes can be represented asvectors using the coordinates of the points in the shape. One shape isaligned to another with a similarity transform (allowing translation,scaling, and rotation) that minimizes the average Euclidean distancebetween shape points. The mean shape is the mean of the aligned trainingshapes.

In some examples, a search is started for landmarks from the mean shapealigned to the position and size of the body/person determined by aglobal body/person detector. Such a search then repeats the steps ofsuggesting a tentative shape by adjusting the locations of shape pointsby template matching of the image texture around each point and thenconforming the tentative shape to a global shape model until convergenceoccurs. In some systems, individual template matches are unreliable, andthe shape model pools the results of the weak template matches to form astronger overall classifier. The entire search is repeated at each levelin an image pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, 3D human poseestimation, 3D body mesh reconstruction, and any other suitable image orvideo manipulation implemented by a convolutional neural network thathas been configured to execute efficiently on the client device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a body/person within theimage or video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's body/person within the image or video stream aspart of the modification operation. Once a modification icon isselected, the transformation system initiates a process to convert theimage of the user to reflect the selected modification icon (e.g.,generate a smiling face on the user). A modified image or video streammay be presented in a graphical user interface displayed on the clientdevice 102 as soon as the image or video stream is captured and aspecified modification is selected. The transformation system mayimplement a complex convolutional neural network on a portion of theimage or video stream to generate and apply the selected modification.That is, the user may capture the image or video stream and be presentedwith a modified result in real-time or near real-time once amodification icon has been selected. Further, the modification may bepersistent while the video stream is being captured and the selectedmodification icon remains toggled. Machine-taught neural networks may beused to enable such modifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the body/personbeing modified by the transformation system and store it for laterviewing or browse to other areas of the imaging application. Wheremultiple faces are modified by the transformation system, the user maytoggle the modification on or off globally by tapping or selecting asingle body/person modified and displayed within a graphical userinterface. In some examples, individual bodies/persons, among a group ofmultiple bodies/persons, may be individually modified, or suchmodifications may be individually toggled by tapping or selecting theindividual body/person or a series of individual bodies/personsdisplayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a created stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location e.g., on a college or university campus) tocontribute to a particular collection. In some examples, a contributionto a location story may require a second degree of authentication toverify that the end user belongs to a specific organization or otherentity (e.g., is a student on the university campus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Trained machine learning technique(s) 307 stores parameters that havebeen trained during training of the intent determination system 224. Forexample, trained machine learning techniques 307 stores the trainedparameters of one or more neural network machine learning techniques.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal, Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

Intent Determination System

FIG. 5 is a block diagram showing an example intent determination system224, according to example examples. Intent determination system 224includes a set of components 510 that operate on a set of input data(e.g., a monocular image/video depicting a real-world object, such as aperson or training data). The set of input data is obtained from one ormore database(s) (FIG. 3 ) during the training phases and is obtainedfrom an RGB camera of a client device 102 when an AR/VR application isbeing used, such as by a messaging client 104. Intent determinationsystem 224 includes a machine learning technique module 512, a skeletalkey-points module 511, a body pose module 514, an image modificationmodule 518, an AR effect module 519, an intent determination module 530,a speech input module 532, a 3D body tracking module 513, a whole-bodysegmentation module 515, and an image display module 520.

During training, the intent determination system 224 receives a giventraining image or video from training data 501. The intent determinationsystem 224 applies one or more machine learning techniques using themachine learning technique module 512 on the given training image orvideo. The machine learning technique module 512 extracts one or morefeatures from the given training image or video to estimate a 3D bodymesh of the person(s) or user(s) depicted in the image or video.

The machine learning technique module 512 retrieves 3D body meshinformation associated with the given training image or video. Themachine learning technique module 512 compares the estimated 3D bodymesh with the ground truth garment 3D body mesh provided as part of thetraining data 502. Based on a difference threshold or deviation of thecomparison, the machine learning technique module 512 updates one ormore coefficients or parameters and obtains one or more additionaltraining images or videos. After a specified number of epochs or batchesof training images have been processed and/or when the differencethreshold or deviation reaches a specified value, the machine learningtechnique module 512 completes training and the parameters andcoefficients of the machine learning technique module 512 are stored inthe trained machine learning technique(s) 307.

In some examples, during training, the machine learning technique module512 receives 2D skeletal joint information from a skeletal key-pointsmodule 511. The skeletal key-points module 511 tracks skeletal keypoints of a user depicted in a given training image (e.g., head joint,shoulder joints, hip joints, leg joints, and so forth) and provides the2D or 3D coordinates of the skeletal key points. This information isused by the machine learning technique module 512 to identifydistinguishing attributes of the user depicted in the training image andto generate the 3D body mesh.

The 3D body mesh generated by the machine learning technique module 512is provided to the body pose module 514. The body pose module 514 cantrack the object depicted in the image or video and update the 3D bodymesh associated with the object. In an example, the body pose module 514can track the object based on 3D body tracking information provided bythe 3D body tracking module 513. The body pose module 514 can determinea pose of the object depicted in the image or video. FIG. 6 is adiagrammatic representation of outputs of the intent determinationsystem 224, in accordance with some examples. Specifically, FIG. 6 showsa 3D body mesh 600 generated and tracked by the body pose module 514. Inone example, the body pose module 514 can track changes of the 3D bodymesh across frames of the video. The body pose module 514 can providechanges to the 3D body mesh to the intent determination module 530 toupdate and deform the external mesh based on changes to the 3D body meshas the 3D body mesh is being blended into the external mesh.

In an example, the body pose module 514 can match a given pose or bodymesh corresponding to an object depicted in the image or video with atarget pose. The body pose module 514 can compare the body mesh orskeletal key points of the object depicted in the image or video with aplurality of different target poses. Each of the plurality of differenttarget poses can be associated with a different intent that can be usedto disambiguate speech input. For example, a first target pose candepict a human body with a finger or hand pointing in a particulardirection. In such cases, the intent associated with the first targetpose can be discerned to be an object, subject, item or direction thatis within a specified range of the particular direction. As explainedbelow, a virtual line can be extended from the finger or hand along theparticular direction. A first object that intersects the virtual linecan be used to discern or disambiguate the intent, such as by replacinga pronoun or adverb with the identity of the first object. As anotherexample, a second target pose can depict a human performing a gesture,such as using their lips or legs. The gesture can be oriented or focusedin a particular direction. In such cases, the intent associated with thesecond target pose can be discerned to be an object, subject, item ordirection that is within a specified range of the particular direction.Again, a virtual line can be extended from the lips or legs along theparticular direction. A second object that intersects the virtual linecan be used to discern or disambiguate the intent, such as by replacinga pronoun or adverb with the identity of the second object.

The intent associated with the pose determined by the body pose module514 can be provided to the intent determination module 530. For example,the body pose module 514 can indicate to the intent determination module530 that the pose represents a person pointing or gesturing towards aparticular direction. The intent determination module 530 cancommunicate with a speech input module 532 to receive a spoken commandfrom a person depicted in the image or video. The speech input module532 can identify an ambiguous portion of the spoken command, such as apronoun or adverb that is included in the spoken command. In some cases,the body pose module 514 can provide a virtual line that is generatedbased on the pose determined for the person depicted in the image orvideo. Namely, the body pose module 514 can generate a virtual line thatextends from a portion of the body mesh along a given direction. Theintent determination module 530 uses the virtual line to identify one ormore objects that intersect the virtual line in the image or video toresolve an ambiguous intent of a spoken command.

The intent determination module 530 can disambiguate the intent of thespoken command based on the intent associated with the pose receivedfrom the body pose module 514. For example, the intent determinationmodule 530 can determine that the ambiguous portion of the spokencommand refers to an object, item, or element depicted in the image orvideo. Specifically, the spoken command can be “change this to blue”. Insuch circumstances, the word “this” can be determined to be a pronounreferring to an unknown object, item or element. The intentdetermination module 530 can process the intent associated with the posereceived from the body pose module 514 to identify the object, item orelement referred to by the word “this”. Namely, the intent determinationmodule 530 can identify an object, item, subject, or element thatintersects a virtual line extending from the finger, hand, or gesture ofthe body pose corresponding to the person depicted in the image orvideo. The intent determination module 530 can substitute the pronoun“this” with the identified object, item, subject or element. Forexample, a hat worn by a speaker of the command can intersect a virtualline drawn from the finger or hand towards a direction pointed to by thefinger or hand. As a result, the intent determination module 530 candetermine that the speech command is requesting to change the hat to thecolor blue (e.g., to change an attribute of the hat).

For example, as shown in FIG. 7 , an image or video 700 depicts a user701 performing a given pose. The body pose module 514 can provide adirection or virtual line 710 that extends from a portion of the user701 towards a particular direction. The intent determination module 530can receive a spoken command, such as “make this blue,” and determinethat the word “this” is ambiguous as to the item, object, subject orthing referenced by the pronoun. The intent determination module 530 canapply the virtual line 710 to the image or video 700 to identify areal-world item that is depicted in the image or video 700 thatintersects the virtual line 710. In this case, the intent determinationmodule 530 identifies a hat 720 worn by the user 701 as intersecting thevirtual line 710. As a result, the intent determination module 530determines that the spoken command requests to change an attribute ofthe hat 720 (e.g., the color of the hat 720) to the color blue. Inresponse, the intent determination module 530 communicates with the AReffect module 519 to apply one or more AR elements to the hat 720 tochange the pixel colors of the real-world hat 720 to the color blue. Asan example, the AR effect module 519 applies a virtual hat to theportion of the image or video 700 that includes the real-world hat 720to replace the real-world hate with the virtual hat 720 that is in thecolor blue. Other attributes can similarly be changed based on speechinput, such as the style, size, pattern, type and so forth. For example,instead of speaking “make this blue” (referring to a color attribute),the speech input can state “make this longer” (referring to a change insize attribute), “make this polka dot” (referring to a change in patternattribute), and/or “make this summery” (referring to change in style).

In an example, the AR effect module 519 selects and applies one or moreAR elements or graphics to an object depicted in the image or videobased on the body mesh associated with the object depicted in the imageor video and based on the resolved voice command provided by the intentdetermination module 530. These AR graphics combined with the real-worldobject depicted in the image or video are provided to the imagemodification module 518 to render an image or video that depicts theperson wearing the AR object, such as an AR purse or earrings. In oneexample, the AR effect module 519 can select between a plurality offashion item segmentation modules based on the command received from theintent determination module 530. Namely, if the intent determinationmodule 530 indicates to the AR effect module 519 that a hat is beingreferenced by a spoken command that includes an ambiguous intent, the AReffect module 519 selects a hat segmentation module. The hatsegmentation module is trained to process an image to identify anddefine borders of a hat type of fashion item. The AR effect module 519can then replace pixels within the segmentation output of the hatsegmentation module with pixels of a virtual object. The AR effectmodule 519 also tracks and modifies a 3D position of the virtual objectbased on the segmentation output of the hat segmentation module. Asanother example, if the intent determination module 530 indicates to theAR effect module 519 that an upper garment or shirt fashion item isbeing referenced by a spoken command that includes an ambiguous intent,the AR effect module 519 selects an upper garment segmentation module.The upper garment segmentation module is trained to process an image toidentify and define borders of an upper garment type of fashion item,such as a blouse, shirt, tank top, sweater and so forth. The AR effectmodule 519 can then replace pixels within the segmentation output of theupper garment segmentation module with pixels of a virtual object. TheAR effect module 519 also tracks and modifies a 3D position of thevirtual object based on the segmentation output of the upper garmentsegmentation module.

The image modification module 518 can adjust the image captured by thecamera based on the AR effect selected by the AR effect module 519. Theimage modification module 518 adjusts the way in which the AR garment(s)or fashion accessory placed over the user or person depicted in theimage or video is/are presented in an image or video, such as bychanging the physical properties (deformation) of the AR garment orfashion accessory based on the changes to the 3D body mesh of the user.Image display module 520 combines the adjustments made by the imagemodification module 518 into the received monocular image or videodepicting the user's body. The image or video is provided by the imagedisplay module 520 to the client device 102 and can then be sent toanother user or stored for later access and display.

In some examples, the image modification module 518 can receive 3D bodytracking information representing the 3D positions of the user depictedin the image from the 3D body tracking module 513. The 3D body trackingmodule 513 generates the 3D body tracking information by processing thetraining data 501 using additional machine learning techniques. Theimage modification module 518 can also receive from another machinelearning technique a whole-body segmentation representing which pixelsin the image correspond to the whole body of the user. The whole-bodysegmentation can be received from the whole-body segmentation module515. The whole-body segmentation module 515 generates the whole-bodysegmentation by processing the training data 501 using a machinelearning technique.

The intent associated with the pose determined by the body pose module514 can be provided to the intent determination module 530. For example,the body pose module 514 can indicate to the intent determination module530 that the pose represents a person pointing or gesturing towards aparticular direction. The intent determination module 530 cancommunicate with a speech input module 532 to receive a spoken commandfrom a person depicted in the image or video. The speech input module532 can identify an ambiguous portion of the spoken command, such as apronoun or adverb that is included in the spoken command. In some cases,the body pose module 514 can provide a virtual line that is generatedbased on the pose determined for the person depicted in the image orvideo. Namely, the body pose module 514 can generate a virtual line thatextends from a portion of the body mesh along a given direction. Theintent determination module 530 uses the virtual line to identify one ormore objects that intersect the virtual line in the image or video toresolve an ambiguous intent of a spoken command.

As another example, the intent determination module 530 can determinethat the ambiguous portion of the spoken command refers to an ambiguouscolor (or other attribute) of a portion of the image or video thatdepicts the speaker of the command. Specifically, the spoken command canbe “change hat to this color”. In such circumstances, the word “this”can be determined to be a pronoun referring to an unknown color value orattribute. The intent determination module 530 can process the intentassociated with the pose received from the body pose module 514 toidentify the color or attribute referred to by the word “this”. Namely,the intent determination module 530 can identify an object, item,subject, or element that intersects a virtual line extending from thefinger, hand, or gesture of the body pose corresponding to the persondepicted in the image or video. The intent determination module 530 cansubstitute the pronoun “this” with the color or attribute of theidentified object, item, subject or element. For example, a hat worn bya speaker of the command can be changed to a red color if the virtualline drawn from the finger or hand towards a direction pointed to by thefinger or hand intersects another depicted object (e.g., a real-worldwall, sky, water, desk, lamp, and so forth) that is of a red color. As aresult, the intent determination module 530 can determine that thespeech command is requesting to change the hat to the color red. In somecases, the intent determination module 530 fails to identify any objectthat intersects the virtual line. In such instances, the intentdetermination module 530 can retrieve a color of a pixel that is withina threshold distance of the finger of the speaker.

As another example, the intent determination module 530 can determinethat the ambiguous portion of the spoken command refers to an ambiguousdirection. Specifically, the spoken command can be “throw ball there”.In such circumstances, the word “there” can be determined to be anadverb referring to an unknown direction. The intent determinationmodule 530 can process the intent associated with the pose received fromthe body pose module 514 to identify the direction referred to by theword “there”. Namely, the intent determination module 530 can obtain avirtual line extending from the finger, hand, or gesture of the bodypose corresponding to the person depicted in the image or video. Theintent determination module 530 can substitute the adverb “there” withthe direction of the virtual line. As a result, the intent determinationmodule 530 can instruct the AR effect module 519 to animate a virtualobject or ball as being thrown or moving along the direction of thevirtual line.

As another example, the intent determination module 530 can determinethat the ambiguous portion of the spoken command refers to an ambiguousperson depicted in a portion of the image or video that depicts thespeaker of the command. Namely, the image or video can depict multiplepeople, and the spoken command can be “show me wearing his shirt”. Insuch circumstances, the word “his” can be determined to be a pronounreferring to an unknown person. The intent determination module 530 canprocess the intent associated with the pose received from the body posemodule 514 to identify the person referred to by the word “his”. Namely,the intent determination module 530 can identify another person (asecond person depicted in the image or video) that intersects a virtualline extending from the finger, hand, or gesture of the body posecorresponding to the person (e.g., a first person speaking the command)depicted in the image or video. The intent determination module 530 cansubstitute the pronoun “his” with the second person.

In response, the intent determination module 530 can instruct the AReffect module 519 to retrieve the identified fashion item (e.g., ashirt) in the spoken command that is worn by the disambiguated person(e.g., the second person). The intent determination module 530 canprovide the UV or 3D coordinates of the second person to the AR effectmodule 519. In response, the AR effect module 519 can obtain asegmentation module corresponding to the shirt, in this case an uppergarment segmentation module. The AR effect module 519 can apply theupper garment segmentation module to the second person depicted in theimage to obtain a segmentation of the shirt worn by the second person.The AR effect module 519 can then generate an AR object representing theshirt worn by the second person by generating an AR shape thatcorresponds to the shirt segmentation and includes pixel values of theshirt worn by the second person. The AR effect module 519 can then applythe generated AR object to the first person, such as by re-scaling andre-sizing the AR object to fit an upper portion of the first person. Asa result of receiving the voice command “show me wearing his shirt”, theAR effect module 519 can render a shirt worn by another person depictedin the image or video as being worn by the speaker of the command.

As shown in 8, image 800 depicts a first real-world person 810 and asecond real-world person 820 captured by a camera of a client device102. A voice or speech input can be received from the first real-worldperson 810 (a user of the client device 102). The voice or speech inputcan be “show me wearing his shirt”. The intent determination module 530can determine that the word “his” is a pronoun making the voice orspeech input have an ambiguous intent. The intent determination module530 can discern that the word “his” refers to the second real-worldperson 820 (non-speaker of the voice or speech input) based on a pose ofthe first real-world person 810 (a finger of the first real-world person810 pointing in the direction of the second real-world person 820). Inresponse, the intent determination module 530 can provide the UV or 3Dcoordinates of the second real-world person 820 to the AR effect module519 and an identification of a target real-world object 830 (e.g., ashirt) which needs to be rendered on the first real-world person 810. Inresponse, the AR effect module 519 can obtain a segmentation of thetarget real-world object 830 and can then apply a generated AR object ofthe target real-world object 830 to the first real-world person 810.

In one implementation, in order to render the target real-world object830 depicted as being worn by the second real-world person 820 as beingworn the first real-world person 810, the AR effect module 519 generatesfirst and second segmentations of corresponding portions of the firstand second real-world persons 810 and 820. Namely, the AR effect module519 segments the fashion item (e.g., a shirt or target real-world object830) worn by the second real-world person 820 to generate a firstsegmentation of the fashion item in response to receiving the speechinput from the first real-world person 810 (that has been disambiguatedas referring to the second real-world person 820). The AR effect module519 segments the fashion item worn (e.g., corresponding to the targetreal-world object 830) by the first real-world person 810 (e.g., theshirt worn by the first real-world person 810) to generate a secondsegmentation of the fashion item in response to receiving the speechinput. The AR effect module 519 obtains pixel values of the fashion itemworn by the second real-world person 820 based on the first segmentationand applies one or more AR elements to the fashion item worn by thefirst real-world person 810 based on the second segmentation of thefashion item and the obtained pixel values. Namely, the AR effect module519 can recolor the pixel values within the second segmentation of thefashion item worn by the first real-world person 810 based on (to matchor mirror) the pixel values within the first segmentation of the fashionitem worn by the second real-world person 820.

FIG. 9 is a flowchart of a process 900 performed by the intentdetermination system 224, in accordance with some examples. Although theflowchart can describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed. A process may correspondto a method, a procedure, and the like. The steps of methods may beperformed in whole or in part, may be performed in conjunction with someor all of the steps in other methods, and may be performed by any numberof different systems or any portion thereof, such as a processorincluded in any of the systems.

At operation 901, the intent determination system 224 (e.g., a clientdevice 102 or a server) receives an image or video that depicts aperson, as discussed above.

At operation 902, the intent determination system 224 identifies a setof skeletal joints of the person, as discussed above.

At operation 903, the intent determination system 224 identifies a poseof the person depicted in the image based on positioning of the set ofskeletal joints, as discussed above.

At operation 904, the intent determination system 224 receives speechinput comprising a request to perform an augmented reality (AR)operation, the speech input comprising an ambiguous intent, as discussedabove.

At operation 905, the intent determination system 224 discerns theambiguous intent of the speech input based on the pose of the persondepicted in the image, as discussed above.

At operation 906, the intent determination system 224, in response toreceiving the speech input, performs the AR operation based ondiscerning the ambiguous intent of the speech input based on the pose ofthe person depicted in the image, as discussed above.

Machine Architecture

FIG. 10 is a diagrammatic representation of the machine 1000 withinwhich instructions 1008 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1000to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1008 may cause the machine 1000to execute any one or more of the methods described herein. Theinstructions 1008 transform the general, non-programmed machine 1000into a particular machine 1000 programmed to carry out the described andillustrated functions in the manner described. The machine 1000 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1000 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1000 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1008, sequentially or otherwise,that specify actions to be taken by the machine 1000. Further, whileonly a single machine 1000 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1008 to perform any one or more of themethodologies discussed herein. The machine 1000, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1000 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1000 may include processors 1002, memory 1004, andinput/output (I/O) components 1038, which may be configured tocommunicate with each other via a bus 1040. In an example, theprocessors 1002 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1006 and a processor 1010 that execute the instructions 1008.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 10 shows multiple processors 1002, the machine 1000 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1004 includes a main memory 1012, a static memory 1014, and astorage unit 1016, all accessible to the processors 1002 via the bus1040. The main memory 1004, the static memory 1014, and the storage unit1016 store the instructions 1008 embodying any one or more of themethodologies or functions described herein. The instructions 1008 mayalso reside, completely or partially, within the main memory 1012,within the static memory 1014, within machine-readable medium 1018within the storage unit 1016, within at least one of the processors 1002(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1038 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1038 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1038 mayinclude many other components that are not shown in FIG. 10 . In variousexamples, the I/O components 1038 may include user output components1024 and user input components 1026. The user output components 1024 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1026 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1038 may include biometriccomponents 1028, motion components 1030, environmental components 1032,or position components 1034, among a wide array of other components. Forexample, the biometric components 1028 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1030 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1032 include, for example, one or morecameras (with still image/photograph and video capabilities),illumination sensor components (e.g., photometer), temperature sensorcomponents (e.g., one or more thermometers that detect ambienttemperature), humidity sensor components, pressure sensor components(e.g., barometer), acoustic sensor components (e.g., one or moremicrophones that detect background noise), proximity sensor components(e.g., infrared sensors that detect nearby objects), gas sensors (e.g.,gas detection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad, or penta rear camera configurations on the front andrear sides of the client device 102, These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1034 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1038 further include communication components 1036operable to couple the machine 1000 to a network 1020 or devices 1022via respective coupling or connections. For example, the communicationcomponents 1036 may include a network interface component or anothersuitable device to interface with the network 1020. In further examples,the communication components 1036 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1022 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1036 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1036 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, Maxi Code, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1036, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1012, static memory 1014, andmemory of the processors 1002) and storage unit 1016 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1008), when executedby processors 1002, cause various operations to implement the disclosedexamples.

The instructions 1008 may be transmitted or received over the network1020, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1036) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 1008may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 1022.

Software Architecture

FIG. 11 is a block diagram 1100 illustrating a software architecture1104, which can be installed on any one or more of the devices describedherein. The software architecture 1104 is supported by hardware such asa machine 1102 that includes processors 1120, memory 1126, and I/Ocomponents 1138. In this example, the software architecture 1104 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1104 includes layerssuch as an operating system 1112, libraries 1110, frameworks 1108, andapplications 1106. Operationally, the applications 1106 invoke API calls1150 through the software stack and receive messages 1152 in response tothe API calls 1150.

The operating system 1112 manages hardware resources and provides commonservices. The operating system 1112 includes, for example, a kernel1114, services 1116, and drivers 1122. The kernel 1114 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1114 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionalities. The services 1116 canprovide other common services for the other software layers. The drivers1122 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1122 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1110 provide a common low-level infrastructure used byapplications 1106. The libraries 1110 can include system libraries 1118(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1110 can include APIlibraries 1124 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4) Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 1110can also include a wide variety of other libraries 1128 to provide manyother APIs to the applications 1106.

The frameworks 1108 provide a common high-level infrastructure that isused by the applications 1106. For example, the frameworks 1108 providevarious graphical user interface functions, high-level resourcemanagement, and high-level location services. The frameworks 1108 canprovide a broad spectrum of other APIs that can be used by theapplications 1106, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1106 may include a home application1136, a contacts application 1130, a browser application 1132, a bookreader application 1134, a location application 1142, a mediaapplication 1144, a messaging application 1146, a game application 1148,and a broad assortment of other applications such as an externalapplication 1140. The applications 1106 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1106, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1140 (e.g., an application developed using the ANDROID™ or IOS™ SDK byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the external application 1140 can invoke the API calls 1150provided by the operating system 1112 to facilitate functionalitydescribed herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistant (PDA), smartphone, tablet, ultrabook, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronics, game console, set-top box, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LIE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein.

Considering examples in which hardware components are temporarily,configured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information)).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1002 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g.; withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

1. A method comprising: receiving, by one or more processors, an imagethat depicts a person and one or more real-world objects comprising abody part; identifying a set of skeletal joints of the person;identifying a captured pose of the person depicted in the image based onpositioning of the set of skeletal joints, the captured pose comprisingthe body part pointing along a particular direction; receiving speechinput comprising a request to perform an augmented reality (AR)operation, the speech input comprising an ambiguous intent; discerningthe ambiguous intent of the speech input based on the captured pose ofthe person depicted in the image; identifying an object, depicted in theimage, that intersects a line extending from the body part along theparticular direction; determining that the ambiguous intent of thespeech input refers to the identified object based on the identifiedobject intersecting the line extending from the body part; andperforming the AR operation based on determining that the ambiguousintent refers to the one or more real-world objects depicted in theimage.
 2. The method of claim 1, wherein the ambiguous intent comprisesa pronoun or adverb, and further comprising identifying a target objector direction corresponding to the pronoun or the adverb based on thecaptured pose of the person depicted in the image, wherein the one ormore real-world objects comprise the target object as the identifiedobject.
 3. The method of claim 2, further comprising: determining thatthe captured pose matches a target pose; and in response to determiningthat the captured pose matches the target pose, determining that thetarget object comprises the person depicted in the image.
 4. The methodof claim 3, further comprising: applying one or more AR elements to theperson depicted in the image in response to receiving the speech inputand in response to determining that the captured pose matches the targetpose.
 5. The method of claim 2, further comprising: determining that thecaptured pose matches a target pose; and in response to determining thatthe captured pose matches the target pose, determining that the targetobject comprises an individual body part of the person depicted in theimage.
 6. The method of claim 5, further comprising: applying one ormore AR elements to the body part of the person depicted in the image inresponse to receiving the speech input and in response to determiningthat the captured pose matches the target pose.
 7. The method of claim6, further comprising: identifying a real-world fashion item depicted ona portion of the image corresponding to the individual body part;segmenting the real-world fashion item depicted in the image to generatea segmentation of the real-world fashion item; and based on thesegmentation of the real-world fashion item, replacing an attribute ofthe real-world fashion item with the one or more AR elements.
 8. Themethod of claim 1, wherein the person is a first person, wherein theimage depicts a second person, wherein the one or more real-worldobjects comprise the second person, further comprising: determining thatthe captured pose matches a target pose; and in response to determiningthat the captured pose matches the target pose, determining that apronoun of the ambiguous intent refers to the second person depicted inthe image.
 9. The method of claim 8, further comprising: applying one ormore AR elements to the second person depicted in the image in responseto receiving the speech input and in response to determining that thecaptured pose matches the target pose.
 10. The method of claim 9,further comprising: determining that the speech input refers to afashion item worn by the second person; segmenting the fashion item wornby the second person to generate a segmentation of the fashion item inresponse to receiving the speech input; and applying the one or more ARelements to the fashion item worn by the second person based on thesegmentation of the fashion item.
 11. The method of claim 8, furthercomprising: applying one or more AR elements to the first persondepicted in the image based on an attribute of a fashion item worn bythe second person depicted in the image in response to receiving thespeech input and in response to determining that the captured posematches the target pose.
 12. The method of claim 11, further comprising:determining that the speech input refers to the fashion item worn by thesecond person; segmenting the fashion item worn by the second person togenerate a first segmentation of the fashion item worn by the secondperson in response to receiving the speech input; segmenting a fashionitem worn by the first person to generate a second segmentation of thefashion item worn by the first person in response to receiving thespeech input; obtaining pixel values of the fashion item worn by thesecond person based on the first segmentation of the fashion item wornby the second person; and applying the one or more AR elements to thefashion item worn by the first person based on the second segmentationof the fashion item worn by the first person and the obtained pixelvalues.
 13. The method of claim 1, further comprising: determining thatthe captured pose matches a target pose; and in response to determiningthat the captured pose matches the target pose, determining that apronoun of the ambiguous intent refers to an attribute of a fashion itemworn by the person depicted in the image, wherein the one or morereal-world objects comprise the fashion item.
 14. The method of claim13, further comprising: determining a pixel value in a portion of theimage pointed to by the target pose; and replacing the attribute of thefashion item worn by the person depicted in the image based on thedetermined pixel value.
 15. The system of claim 19, wherein theambiguous intent comprises a pronoun or adverb, and further comprisingidentifying a target object or direction corresponding to the pronoun orthe adverb based on the captured pose of the person depicted in theimage, wherein the one or more real-world objects comprise the targetobject.
 16. The method of claim 1, wherein the identified objectcomprises at least one of a first fashion item worn by the persondepicted in the image, a second fashion item worn by a second persondepicted in the image, the second person, or a color of a pixel in theimage.
 17. The method of claim 1, wherein the request to perform the ARoperation comprises at least one of adding a fashion item to the persondepicted in the image or changing a color or attribute of a givenfashion item worn by the person depicted in the image.
 18. The method ofclaim 1, wherein the captured pose represents a shape, wherein a pronounof the ambiguous intent is resolved based on the shape represented bythe captured pose, further comprising presenting one or more AR elementsbased on the shape represented by the captured pose in response toreceiving the speech input.
 19. A system comprising: one or moreprocessors; and a memory for storing instructions for execution by theone or more processors configured to perform operations comprising:receiving an image that depicts a person and one or more real-worldobjects comprising a body part; identifying a set of skeletal joints ofthe person; identifying a captured pose of the person depicted in theimage based on positioning of the set of skeletal joints, the capturedpose comprising the body part pointing along a particular direction;receiving speech input comprising a request to perform an augmentedreality (AR) operation, the speech input comprising an ambiguous intent;discerning the ambiguous intent of the speech input based on thecaptured pose of the person depicted in the image; identifying anobject, depicted in the image, that intersects a line extending from thebody part along the particular direction; determining that the ambiguousintent of the speech input refers to the identified object based on theidentified object intersecting the line extending from the body part;and performing the AR operation based on determining that the ambiguousintent refers to the one or more real-world objects depicted in theimage.
 20. A non-transitory machine-readable storage medium includinginstructions that, when executed by one or more processors of a machine,cause the machine to perform operations comprising: receiving an imagethat depicts a person and one or more real-world objects comprising abody part; identifying a set of skeletal joints of the person;identifying a captured pose of the person depicted in the image based onpositioning of the set of skeletal joints, the captured pose comprisingthe body part pointing along a particular direction; receiving speechinput comprising a request to perform an augmented reality (AR)operation, the speech input comprising an ambiguous intent; discerningthe ambiguous intent of the speech input based on the captured pose ofthe person depicted in the image; identifying an object, depicted in theimage, that intersects a line extending from the body part along theparticular direction; determining that the ambiguous intent of thespeech input refers to the identified object based on the identifiedobject intersecting the line extending from the body part; andperforming the AR operation based on determining that the ambiguousintent refers to the one or more real-world objects depicted in theimage.